Overview

Dataset statistics

Number of variables12
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.9 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

Dp1 is highly overall correlated with Dp2 and 7 other fieldsHigh correlation
Dp2 is highly overall correlated with Dp1 and 7 other fieldsHigh correlation
Dp3 is highly overall correlated with Dp1 and 1 other fieldsHigh correlation
Dp4Plus is highly overall correlated with Dp1 and 6 other fieldsHigh correlation
Extrato is highly overall correlated with Dp1 and 7 other fieldsHigh correlation
MashingEfficiency is highly overall correlated with Dp2 and 4 other fieldsHigh correlation
PercFermentaveis is highly overall correlated with Dp1 and 7 other fieldsHigh correlation
SolidosFermentaveis is highly overall correlated with Dp1 and 7 other fieldsHigh correlation
SolidosNaoFermentaveis is highly overall correlated with Dp1 and 6 other fieldsHigh correlation
SolidosTotais is highly overall correlated with Dp1 and 7 other fieldsHigh correlation
Temperature is highly overall correlated with Dp3High correlation
Temperature has unique valuesUnique
MashTime has unique valuesUnique
SolidosFermentaveis has unique valuesUnique
SolidosNaoFermentaveis has unique valuesUnique
SolidosTotais has unique valuesUnique
PercFermentaveis has unique valuesUnique
Extrato has unique valuesUnique
MashingEfficiency has unique valuesUnique
Dp1 has unique valuesUnique
Dp2 has unique valuesUnique
Dp4Plus has unique valuesUnique
Dp3 has 11 (1.1%) zerosZeros

Reproduction

Analysis started2024-03-10 12:56:06.149710
Analysis finished2024-03-10 12:56:23.329994
Duration17.18 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Temperature
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65
Minimum32.094733
Maximum97.905267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:23.432715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum32.094733
5-th percentile48.594903
Q158.262961
median65
Q371.737039
95-th percentile81.405097
Maximum97.905267
Range65.810535
Interquartile range (IQR)13.474077

Descriptive statistics

Standard deviation9.9984947
Coefficient of variation (CV)0.153823
Kurtosis-0.021819128
Mean65
Median Absolute Deviation (MAD)6.7449059
Skewness8.8708768 × 10-16
Sum65000
Variance99.969896
MonotonicityNot monotonic
2024-03-10T09:56:23.615252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.61857687 1
 
0.1%
80.0237612 1
 
0.1%
57.46251078 1
 
0.1%
70.96262318 1
 
0.1%
51.24575895 1
 
0.1%
70.84327491 1
 
0.1%
64.56119928 1
 
0.1%
52.04071154 1
 
0.1%
70.93270706 1
 
0.1%
69.30268965 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
32.09473269 1
0.1%
35.32262075 1
0.1%
36.92966232 1
0.1%
38.03155739 1
0.1%
38.87945859 1
0.1%
39.57301181 1
0.1%
40.16230707 1
0.1%
40.67620941 1
0.1%
41.13292266 1
0.1%
41.54469029 1
0.1%
ValueCountFrequency (%)
97.90526731 1
0.1%
94.67737925 1
0.1%
93.07033768 1
0.1%
91.96844261 1
0.1%
91.12054141 1
0.1%
90.42698819 1
0.1%
89.83769293 1
0.1%
89.32379059 1
0.1%
88.86707734 1
0.1%
88.45530971 1
0.1%

MashTime
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100
Minimum1.2841981
Maximum198.7158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:23.778806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.2841981
5-th percentile50.78471
Q179.788884
median100
Q3120.21112
95-th percentile149.21529
Maximum198.7158
Range197.4316
Interquartile range (IQR)40.422232

Descriptive statistics

Standard deviation29.995484
Coefficient of variation (CV)0.29995484
Kurtosis-0.021819128
Mean100
Median Absolute Deviation (MAD)20.234718
Skewness9.0858546 × 10-16
Sum100000
Variance899.72906
MonotonicityNot monotonic
2024-03-10T09:56:23.941359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.96240057 1
 
0.1%
95.9647264 1
 
0.1%
141.4572352 1
 
0.1%
73.82853907 1
 
0.1%
42.43371321 1
 
0.1%
59.12118096 1
 
0.1%
107.0194195 1
 
0.1%
95.81290968 1
 
0.1%
152.0061551 1
 
0.1%
91.26798694 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1.284198055 1
0.1%
10.96786224 1
0.1%
15.78898695 1
0.1%
19.09467217 1
0.1%
21.63837576 1
0.1%
23.71903542 1
0.1%
25.4869212 1
0.1%
27.02862824 1
0.1%
28.39876797 1
0.1%
29.63407088 1
0.1%
ValueCountFrequency (%)
198.7158019 1
0.1%
189.0321378 1
0.1%
184.2110131 1
0.1%
180.9053278 1
0.1%
178.3616242 1
0.1%
176.2809646 1
0.1%
174.5130788 1
0.1%
172.9713718 1
0.1%
171.601232 1
0.1%
170.3659291 1
0.1%

SolidosFermentaveis
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.94093
Minimum15.507055
Maximum114.56959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:24.104978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum15.507055
5-th percentile45.228111
Q189.739332
median105.87689
Q3112.29429
95-th percentile114.47167
Maximum114.56959
Range99.062535
Interquartile range (IQR)22.554955

Descriptive statistics

Standard deviation22.094885
Coefficient of variation (CV)0.22792112
Kurtosis2.8778938
Mean96.94093
Median Absolute Deviation (MAD)7.7436652
Skewness-1.8092745
Sum96940.93
Variance488.18395
MonotonicityNot monotonic
2024-03-10T09:56:24.254570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.68760203 1
 
0.1%
74.92611748 1
 
0.1%
106.7941341 1
 
0.1%
105.1735828 1
 
0.1%
57.35158296 1
 
0.1%
105.5355173 1
 
0.1%
114.5396983 1
 
0.1%
91.42610926 1
 
0.1%
105.2651297 1
 
0.1%
109.624056 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
15.50705457 1
0.1%
15.6668615 1
0.1%
16.49522823 1
0.1%
16.72802098 1
0.1%
17.42776874 1
0.1%
17.52030179 1
0.1%
17.64865312 1
0.1%
18.09753966 1
0.1%
18.26435726 1
0.1%
20.18592404 1
0.1%
ValueCountFrequency (%)
114.5695897 1
0.1%
114.5695655 1
0.1%
114.5692976 1
0.1%
114.5692492 1
0.1%
114.5686611 1
0.1%
114.5679453 1
0.1%
114.5677831 1
0.1%
114.5668054 1
0.1%
114.5653536 1
0.1%
114.5650649 1
0.1%

SolidosNaoFermentaveis
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.490402
Minimum13.594282
Maximum85.742943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:24.400173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum13.594282
5-th percentile38.745477
Q140.111739
median44.863975
Q351.427665
95-th percentile78.466621
Maximum85.742943
Range72.148661
Interquartile range (IQR)11.315927

Descriptive statistics

Standard deviation13.317247
Coefficient of variation (CV)0.27463675
Kurtosis1.1703495
Mean48.490402
Median Absolute Deviation (MAD)5.3389625
Skewness0.9285971
Sum48490.402
Variance177.34906
MonotonicityNot monotonic
2024-03-10T09:56:24.558709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.038348 1
 
0.1%
76.00243532 1
 
0.1%
46.16285134 1
 
0.1%
47.70972982 1
 
0.1%
42.45204048 1
 
0.1%
47.36431145 1
 
0.1%
38.7737376 1
 
0.1%
50.99796873 1
 
0.1%
47.62241994 1
 
0.1%
43.46364351 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
13.59428173 1
0.1%
14.50329035 1
0.1%
14.54883466 1
0.1%
14.86696525 1
0.1%
14.99360942 1
0.1%
15.72699931 1
0.1%
15.91022159 1
0.1%
16.54875013 1
0.1%
17.45926835 1
0.1%
17.80905359 1
0.1%
ValueCountFrequency (%)
85.74294278 1
0.1%
85.73278961 1
0.1%
85.72144099 1
0.1%
85.68256833 1
0.1%
85.67184717 1
0.1%
85.60860283 1
0.1%
85.59248603 1
0.1%
85.49655865 1
0.1%
85.44277721 1
0.1%
85.38139625 1
0.1%

SolidosTotais
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.46159
Minimum148.10697
Maximum153.3148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:24.737251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum148.10697
5-th percentile149.7684
Q1152.16374
median152.91406
Q3153.21034
95-th percentile153.31032
Maximum153.3148
Range5.2078293
Interquartile range (IQR)1.046608

Descriptive statistics

Standard deviation1.1233287
Coefficient of variation (CV)0.0073679456
Kurtosis3.7114896
Mean152.46159
Median Absolute Deviation (MAD)0.35822169
Skewness-1.9966588
Sum152461.59
Variance1.2618674
MonotonicityNot monotonic
2024-03-10T09:56:24.898790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151.8035279 1
 
0.1%
151.4811309 1
 
0.1%
152.9578143 1
 
0.1%
152.8833479 1
 
0.1%
150.4245233 1
 
0.1%
152.8999632 1
 
0.1%
153.3134364 1
 
0.1%
152.2040678 1
 
0.1%
152.8875525 1
 
0.1%
153.0877018 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
148.1069734 1
0.1%
148.1173602 1
0.1%
148.1707605 1
0.1%
148.1856345 1
0.1%
148.2299926 1
0.1%
148.235819 1
0.1%
148.2438855 1
0.1%
148.2719577 1
0.1%
148.282335 1
0.1%
148.3960277 1
0.1%
ValueCountFrequency (%)
153.3148028 1
0.1%
153.3148021 1
0.1%
153.3147887 1
0.1%
153.3147879 1
0.1%
153.3147614 1
0.1%
153.3147261 1
0.1%
153.3147215 1
0.1%
153.3146732 1
0.1%
153.3146058 1
0.1%
153.3145965 1
0.1%

PercFermentaveis
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.479269
Minimum10.470172
Maximum74.728329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:25.121174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10.470172
5-th percentile30.198701
Q158.987291
median69.238796
Q373.294195
95-th percentile74.666644
Maximum74.728329
Range64.258157
Interquartile range (IQR)14.306904

Descriptive statistics

Standard deviation14.222057
Coefficient of variation (CV)0.22404254
Kurtosis3.022689
Mean63.479269
Median Absolute Deviation (MAD)4.8963729
Skewness-1.8409478
Sum63479.269
Variance202.2669
MonotonicityNot monotonic
2024-03-10T09:56:25.269801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.81139897 1
 
0.1%
49.46234362 1
 
0.1%
69.81933853 1
 
0.1%
68.79335401 1
 
0.1%
38.12648476 1
 
0.1%
69.02259169 1
 
0.1%
74.70949774 1
 
0.1%
60.06811157 1
 
0.1%
68.85134076 1
 
0.1%
71.60866267 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
10.4701718 1
0.1%
10.57732968 1
0.1%
11.13257985 1
0.1%
11.28855779 1
0.1%
11.75724861 1
0.1%
11.81920936 1
0.1%
11.90514743 1
0.1%
12.20563885 1
0.1%
12.31728463 1
0.1%
13.60273881 1
0.1%
ValueCountFrequency (%)
74.72832867 1
0.1%
74.72831319 1
0.1%
74.72814498 1
0.1%
74.72811382 1
0.1%
74.72774315 1
0.1%
74.72729345 1
0.1%
74.72718993 1
0.1%
74.72657572 1
0.1%
74.72566163 1
0.1%
74.7254779 1
0.1%

Extrato
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.543133
Minimum3.6006973
Maximum15.33148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:25.423379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3.6006973
5-th percentile9.6011719
Q115.151103
median15.286984
Q315.320537
95-th percentile15.330998
Maximum15.33148
Range11.730783
Interquartile range (IQR)0.16943371

Descriptive statistics

Standard deviation2.2758429
Coefficient of variation (CV)0.15648917
Kurtosis12.895315
Mean14.543133
Median Absolute Deviation (MAD)0.042044387
Skewness-3.6633523
Sum14543.133
Variance5.1794608
MonotonicityNot monotonic
2024-03-10T09:56:25.576964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.172595 1
 
0.1%
15.09285528 1
 
0.1%
15.29569855 1
 
0.1%
15.28833126 1
 
0.1%
9.980362344 1
 
0.1%
15.28998287 1
 
0.1%
15.33134359 1
 
0.1%
14.2424078 1
 
0.1%
15.28875497 1
 
0.1%
15.30876995 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
3.600697344 1
0.1%
3.601736024 1
0.1%
3.607076055 1
0.1%
3.608563448 1
0.1%
3.612999259 1
0.1%
3.613581898 1
0.1%
3.614388547 1
0.1%
3.617195766 1
0.1%
3.618233502 1
0.1%
3.632438132 1
0.1%
ValueCountFrequency (%)
15.33148027 1
0.1%
15.33148021 1
0.1%
15.33147877 1
0.1%
15.33147871 1
0.1%
15.33147614 1
0.1%
15.3314726 1
0.1%
15.33147215 1
0.1%
15.33146726 1
0.1%
15.33146027 1
0.1%
15.33145862 1
0.1%

MashingEfficiency
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.29886
Minimum24.311464
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:25.743510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum24.311464
5-th percentile63.893261
Q199.648003
median99.999165
Q399.999996
95-th percentile100
Maximum100
Range75.688536
Interquartile range (IQR)0.35199248

Descriptive statistics

Standard deviation14.647612
Coefficient of variation (CV)0.15370186
Kurtosis13.266651
Mean95.29886
Median Absolute Deviation (MAD)0.00083529897
Skewness-3.7159142
Sum95298.86
Variance214.55254
MonotonicityNot monotonic
2024-03-10T09:56:25.897091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.94889589 1
 
0.1%
99.63521656 1
 
0.1%
99.99945817 1
 
0.1%
99.99997689 1
 
0.1%
66.34797388 1
 
0.1%
99.99991207 1
 
0.1%
99.99999967 1
 
0.1%
93.57442284 1
 
0.1%
99.99999817 1
 
0.1%
99.99999848 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
24.31146394 1
0.1%
24.31677163 1
0.1%
24.34404765 1
0.1%
24.35164151 1
0.1%
24.37427943 1
0.1%
24.37725189 1
0.1%
24.3813668 1
0.1%
24.3956836 1
0.1%
24.40097468 1
0.1%
24.47332507 1
0.1%
ValueCountFrequency (%)
100 1
0.1%
100 1
0.1%
100 1
0.1%
100 1
0.1%
100 1
0.1%
100 1
0.1%
100 1
0.1%
100 1
0.1%
100 1
0.1%
100 1
0.1%

Dp1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9136372
Minimum3.462543
Maximum10.749332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:26.051668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3.462543
5-th percentile6.7245827
Q17.1999329
median7.6707357
Q38.3160781
95-th percentile10.492325
Maximum10.749332
Range7.2867894
Interquartile range (IQR)1.1161452

Descriptive statistics

Standard deviation1.250674
Coefficient of variation (CV)0.15804035
Kurtosis1.6030465
Mean7.9136372
Median Absolute Deviation (MAD)0.52420514
Skewness0.066429133
Sum7913.6372
Variance1.5641855
MonotonicityNot monotonic
2024-03-10T09:56:26.218182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.20052999 1
 
0.1%
10.67232086 1
 
0.1%
7.819734658 1
 
0.1%
7.973060667 1
 
0.1%
7.16111765 1
 
0.1%
7.938585334 1
 
0.1%
7.085051185 1
 
0.1%
8.287024566 1
 
0.1%
7.964368186 1
 
0.1%
7.550346322 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
3.462542996 1
0.1%
3.490462647 1
0.1%
3.628862736 1
0.1%
3.665848006 1
0.1%
3.771984011 1
0.1%
3.785454039 1
0.1%
3.803919955 1
0.1%
3.86651063 1
0.1%
3.888983532 1
0.1%
4.112145 1
0.1%
ValueCountFrequency (%)
10.74933243 1
0.1%
10.74884513 1
0.1%
10.74855005 1
0.1%
10.74786337 1
0.1%
10.74708591 1
0.1%
10.74586611 1
0.1%
10.74444097 1
0.1%
10.74305496 1
0.1%
10.74037715 1
0.1%
10.73951125 1
0.1%

Dp2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.497758
Minimum7.0076288
Maximum56.951113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:26.377782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7.0076288
5-th percentile16.040312
Q139.589438
median50.652719
Q355.294126
95-th percentile56.879715
Maximum56.951113
Range49.943484
Interquartile range (IQR)15.704687

Descriptive statistics

Standard deviation12.806938
Coefficient of variation (CV)0.28148503
Kurtosis0.72169664
Mean45.497758
Median Absolute Deviation (MAD)5.5600026
Skewness-1.3022076
Sum45497.758
Variance164.01765
MonotonicityNot monotonic
2024-03-10T09:56:26.527339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.81679292 1
 
0.1%
28.01884042 1
 
0.1%
51.27964517 1
 
0.1%
50.09503726 1
 
0.1%
24.98715613 1
 
0.1%
50.35992524 1
 
0.1%
56.92927459 1
 
0.1%
41.94784575 1
 
0.1%
50.16200833 1
 
0.1%
53.34737405 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
7.007628806 1
0.1%
7.086867029 1
0.1%
7.503717115 1
0.1%
7.622709784 1
0.1%
7.9852646 1
0.1%
8.033755322 1
0.1%
8.101227471 1
0.1%
8.339128217 1
0.1%
8.428301101 1
0.1%
8.990173676 1
0.1%
ValueCountFrequency (%)
56.95111272 1
0.1%
56.95108913 1
0.1%
56.9509099 1
0.1%
56.95085391 1
0.1%
56.9504193 1
0.1%
56.94993323 1
0.1%
56.94977459 1
0.1%
56.9491113 1
0.1%
56.9480653 1
0.1%
56.9478002 1
0.1%

Dp3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct990
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.067874
Minimum0
Maximum10.794517
Zeros11
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:26.678925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.6304503
Q110.695077
median10.702353
Q310.721364
95-th percentile10.786694
Maximum10.794517
Range10.794517
Interquartile range (IQR)0.026287427

Descriptive statistics

Standard deviation2.0755306
Coefficient of variation (CV)0.20615381
Kurtosis13.4046
Mean10.067874
Median Absolute Deviation (MAD)0.016853952
Skewness-3.7360034
Sum10067.874
Variance4.3078271
MonotonicityNot monotonic
2024-03-10T09:56:26.851455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
1.1%
10.79407605 1
 
0.1%
10.69557359 1
 
0.1%
9.833241255 1
 
0.1%
10.72496424 1
 
0.1%
10.71094229 1
 
0.1%
10.6971341 1
 
0.1%
10.70473455 1
 
0.1%
10.59365466 1
 
0.1%
10.70306999 1
 
0.1%
Other values (980) 980
98.0%
ValueCountFrequency (%)
0 11
1.1%
5.787527379 × 10-51
 
0.1%
0.0191066211 1
 
0.1%
0.07541608739 1
 
0.1%
0.1190344706 1
 
0.1%
0.1329857435 1
 
0.1%
0.1880867197 1
 
0.1%
0.3291524719 1
 
0.1%
0.4341653165 1
 
0.1%
0.4855816339 1
 
0.1%
ValueCountFrequency (%)
10.79451731 1
0.1%
10.79450441 1
0.1%
10.79450365 1
0.1%
10.79446205 1
0.1%
10.7943913 1
0.1%
10.79429104 1
0.1%
10.7942079 1
0.1%
10.794151 1
0.1%
10.7940931 1
0.1%
10.79407605 1
0.1%

Dp4Plus
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.819591
Minimum9.1508862
Maximum57.032549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-10T09:56:27.005067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.1508862
5-th percentile25.271846
Q126.20262
median29.346081
Q333.723286
95-th percentile51.854018
Maximum57.032549
Range47.881663
Interquartile range (IQR)7.5206656

Descriptive statistics

Standard deviation8.8673111
Coefficient of variation (CV)0.27867458
Kurtosis1.2064355
Mean31.819591
Median Absolute Deviation (MAD)3.5451727
Skewness0.99750487
Sum31819.591
Variance78.629207
MonotonicityNot monotonic
2024-03-10T09:56:27.234443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.13749693 1
 
0.1%
50.17287294 1
 
0.1%
30.18011964 1
 
0.1%
31.20662287 1
 
0.1%
28.22148912 1
 
0.1%
30.97732038 1
 
0.1%
25.29050193 1
 
0.1%
33.50631127 1
 
0.1%
31.14865741 1
 
0.1%
28.39133581 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
9.15088622 1
0.1%
9.76645211 1
0.1%
9.794301337 1
0.1%
9.99541676 1
0.1%
10.09791486 1
0.1%
10.59596757 1
0.1%
10.7149722 1
0.1%
11.1313263 1
0.1%
11.76532056 1
0.1%
12.0000747 1
0.1%
ValueCountFrequency (%)
57.03254908 1
0.1%
57.02186138 1
0.1%
57.01737893 1
0.1%
56.99095471 1
0.1%
56.97676026 1
0.1%
56.94212472 1
0.1%
56.89719717 1
0.1%
56.88528174 1
0.1%
56.79577114 1
0.1%
56.78537983 1
0.1%

Interactions

2024-03-10T09:56:21.541873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:06.331177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.775350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.199500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.507932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.961963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.379066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.735366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.163505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.540749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.953893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.245446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.671521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:06.452846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.895986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.316150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.624581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.077644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.504723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.850053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.283144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.659422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.062591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.356231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.784247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:06.577537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.014662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.434865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.747278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.205301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.631392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.971735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.403849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.776107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.178353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.475816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.897901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:06.684243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.126357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.531598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.852960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.316001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.736127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.153244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.519532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.880818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.277088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.591499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.015628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:06.803917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.258996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.644287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.980639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.439661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.850812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.270938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.638177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.999495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.389776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.705157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.122290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:06.915612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.375677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.749005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.096329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.545374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.960512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.376618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.748904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.110167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.503434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.809901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.238007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.035254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.501373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.860666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.280791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.654076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.068201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.500280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.863561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.226874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.619149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.917608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.344719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.143956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.610109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.963418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.406449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.761750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.177919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.601037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.974258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.333551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.717884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.019331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.463404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.270612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.731745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.080100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.525156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:12.877467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.295599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.725666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.090975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.448267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.827581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.133020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.668830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.394354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.868404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.189800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.642804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.008112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.420269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.840353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.210647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.643737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:19.939277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.239699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.763573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.500067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:08.979071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.301463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.746550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.131743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.525972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:15.940116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.319350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.745429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.035016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.338460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:22.874284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:07.609766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:09.090767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:10.407206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:11.858217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:13.257399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:14.630685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:16.040831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:17.429061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:18.850176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:20.135710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T09:56:21.439155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-10T09:56:27.350095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Dp1Dp2Dp3Dp4PlusExtratoMashTimeMashingEfficiencyPercFermentaveisSolidosFermentaveisSolidosNaoFermentaveisSolidosTotaisTemperature
Dp11.000-0.6210.5470.964-0.5490.005-0.365-0.608-0.6080.966-0.6050.473
Dp2-0.6211.000-0.061-0.6930.9630.0560.7960.9990.999-0.6870.999-0.129
Dp30.547-0.0611.0000.4650.1130.1170.297-0.040-0.0400.472-0.0340.589
Dp4Plus0.964-0.6930.4651.000-0.6220.001-0.439-0.680-0.6801.000-0.6780.488
Extrato-0.5490.9630.113-0.6221.0000.1140.8720.9700.970-0.6140.9720.057
MashTime0.0050.0560.1170.0010.1141.0000.3220.0620.0620.0030.063-0.006
MashingEfficiency-0.3650.7960.297-0.4390.8720.3221.0000.8050.805-0.4310.8080.268
PercFermentaveis-0.6080.999-0.040-0.6800.9700.0620.8051.0001.000-0.6741.000-0.104
SolidosFermentaveis-0.6080.999-0.040-0.6800.9700.0620.8051.0001.000-0.6741.000-0.103
SolidosNaoFermentaveis0.966-0.6870.4721.000-0.6140.003-0.431-0.674-0.6741.000-0.6710.493
SolidosTotais-0.6050.999-0.034-0.6780.9720.0630.8081.0001.000-0.6711.000-0.098
Temperature0.473-0.1290.5890.4880.057-0.0060.268-0.104-0.1030.493-0.0981.000

Missing values

2024-03-10T09:56:23.017877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-10T09:56:23.222323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TemperatureMashTimeSolidosFermentaveisSolidosNaoFermentaveisSolidosTotaisPercFermentaveisExtratoMashingEfficiencyDp1Dp2Dp3Dp4Plus
077.61857799.96240181.68760270.038348151.80352853.81139915.17259599.94889610.20053032.81679310.79407646.137497
170.99259365.122604105.08140147.797636152.87911468.73496215.28790499.9999507.98180450.02760910.72554931.264988
283.05477569.60622164.99649582.882214150.95560943.05669414.78787197.96171810.58142421.91119110.56407954.905024
380.260396109.67792874.21749676.579335151.44571549.00600515.07968399.57154010.69770727.54389910.76439950.565534
460.252989134.292789110.88290842.262640153.14555372.40360915.31455599.9999967.43164754.26506710.70689627.596387
562.53118699.661598113.41833339.843621153.26196674.00292215.32619599.9999927.19141356.11274610.69876325.997070
663.01942473.496878113.81147239.468160153.28001274.25069415.32796399.9997527.15411056.39911610.69746825.749058
772.33915961.808475100.57647852.095488152.67224765.87738115.26719799.9998168.41069346.72662610.74006234.122434
875.38580284.05186789.07345863.063832152.14385958.54554915.21372999.9956829.50859438.26019810.77675741.450133
967.96301372.251966112.22380740.983248153.20707173.24975715.32070699.9999907.30387355.24328810.70259626.750233
TemperatureMashTimeSolidosFermentaveisSolidosNaoFermentaveisSolidosTotaisPercFermentaveisExtratoMashingEfficiencyDp1Dp2Dp3Dp4Plus
99070.90284469.353884105.35582847.535839152.89171668.90878815.28916799.9999687.95572250.22839810.72466831.091181
99170.665794104.339014106.06343546.860769152.92420969.35686415.29242099.9999977.88847150.74600010.72239330.643133
99265.51408558.542765114.48703338.823342153.31098874.67633915.33103899.9996007.08959156.89146310.69528425.323262
99368.35828865.562284111.55358241.622673153.17629572.82692315.31762599.9999747.36734954.75483010.70474427.173051
99451.436883130.64611693.90484851.928247152.33403561.64403614.58330995.7324448.38556943.11875910.13970934.088408
99558.364838132.748611108.17676344.844237153.02130070.69392515.30210099.9998047.68842052.28994310.71556229.305879
99655.03485095.204529102.52130549.469456152.75791667.11357915.19907699.4977978.14908748.30388410.66060832.384218
99783.452581126.95044363.65358583.551524150.88006642.18820114.72051197.56431910.49796321.17884810.51138955.376118
99842.265653174.51307922.86330313.594282148.55699715.3902563.64575824.5411434.34525311.0449450.0000589.150886
99943.67916764.06639420.40712917.809054148.40785613.7507073.82161825.7507814.1341479.4284730.18808712.000075